FESOM2 simulations with increasing sea-ice model complexity under different atmospheric forcings

<p><strong>Introduction</strong></p> <p>This dataset has been compiled in support of the paper &quot;Impact of sea-ice model complexity on the performance of an unstructured sea-ice/ocean model under different atmospheric forcings&quot; by Zampieri et al., submitted to the Journal of Advances in Modeling Earth Systems (JAMES) published by the American Geophysical Union (AGU).</p> <p><strong>Scientific description of the dataset</strong></p> <p>The dataset contains the results of sea-ice simulations performed with the Finite-volumE Sea ice-Ocean Model version 2 (FESOM2), based on six model configurations: C1-E, C1-N, C2-E, C2-N, C3-E, and C3-N. As described in the paper, the complexity of the sea-ice model increases from the setup C1 to C3. The suffix -E and -N indicate respectively the ERA5 and NCEP atmospheric forcings used as boundary conditions for the FESOM2 model. As two iterations of the Green&#39;s function approach for the optimization of the parameter space have been performed, each configuration features three separate simulations: a control run (cnt), a first-round of optimization (opt_1), and a second and final round of optimization (opt_2). The parameter optimization is based on various sea-ice observations retrieved over the period 2002&ndash;2015. In total, 18 simulations compose the dataset (6 configurations x 3 realizations). The following 2D monthly-averaged variables are provided: the sea-ice concentration, the sea-ice thickness, the meridional and zonal components of the sea-ice velocity, and the snow thickness on top of the sea ice. The fields are defined on a global unstructured mesh denominated &quot;CORE2&quot;, which is also included in the database.</p> <p><strong>Technical description of the dataset</strong></p> <p>As an unstructured model output is not widely diffused in the sea-ice community, we include here some suggestions for handling and analyzing the simulation results.</p> <p>The files can be interpolated to a regular grid using the following <strong><a href="https://code.mpimet.mpg.de/projects/cdo">CDO</a></strong> commands:</p> <ol> <li>Add grid description to model file:&nbsp;<strong><em>cdo setgrid,CORE2_mesh.nc var.fesom.yyyy.nc temp.nc</em></strong></li> <li>Interpolate to regular grid:&nbsp;<strong>cdo remapycon,r360x180 temp.nc var.fesom.interpolated.yyyy.nc</strong></li> </ol> <p>Furthermore, the python package<strong> <a href="https://code.mpimet.mpg.de/projects/cdo">pyfesom2</a></strong> can be used for plotting the unstructured model data and for interpolating it to a regular grid. The R package&nbsp;<strong><a href="https://github.com/FESOM/spheRlab">spheRlab</a></strong> can be used for plotting the model data directly on its unstructured grid and for performing further analysis. More information can be found on the <strong><a href="https://fesom.de/cmip6/work-with-awi-cm-unstructured-data/">FESOM website</a></strong>.</p> <p>The following naming convention is adopted for the model variables:</p> <ul> <li><strong><em>a_ice</em></strong>&nbsp;&rarr; sea-ice concentration</li> <li><strong><em>m_ice</em></strong>&nbsp;&rarr; sea-ice volume per unit area of ice</li> <li><strong><em>m_snow&nbsp;</em></strong>&rarr; snow-volume per unit area of ice</li> <li><strong><em>vice</em></strong>&nbsp;&rarr; meridional component of the sea-ice velocity</li> <li><strong><em>uice</em></strong>&nbsp;&rarr; zonal component of the sea-ice velocity</li> </ul> <p>Three types of simulation are included:</p> <ul> <li><strong>cnt&nbsp;</strong>&rarr; control run before the parameters optimization (2000&ndash;2019)</li> <li><strong>opt_1&nbsp;</strong>&rarr; after the first iteration of the parameter optimization method (2000&ndash;2015)</li> <li><strong>opt_2</strong>&nbsp;&rarr; after the second iteration of the parameter optimization method (2000&ndash;2019)</li> </ul> <p>Do not hesitate to contact the corresponding author (lorenzo.zampieri@awi.de) for additional information about the data processing and for any other issue with this dataset.</p> <p>&nbsp;</p> <p>&nbsp;</p>